{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# nurseCharting\n",
    "\n",
    "The nurseCharting table is the largest table in eICU-CRD, and contains information entered in a semi-structured form by care staff. The three columns `nursingchartcelltypecat`, `nursingchartcelltypevallabel` and `nursingchartcelltypevalname` provide an organised structure for the data, but values entered in `nursingchartvalue` are free text entry and therefore fairly unstructured. Nurse charting data can be entered directy into the system or can represent interfaced data from charting in the bedside EMR.\n",
    "\n",
    "At the moment, publicly available data for this table has been chosen based upon whether the information in `nursingchartvalue` is structured: data where this is highly structured has been made available (including scores such as the Glasgow Coma Scale or vital signs such as heart rate), conversely data where this is highly unstructured (free-text comments, nursing assessments) are not currently publicly available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alistairewj/.local/lib/python3.5/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
      "  \"\"\")\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import psycopg2\n",
    "import getpass\n",
    "\n",
    "# for configuring connection \n",
    "from configobj import ConfigObj\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database: eicu\n",
      "Username: alistairewj\n"
     ]
    }
   ],
   "source": [
    "# Create a database connection using settings from config file\n",
    "config='../db/config.ini'\n",
    "\n",
    "# connection info\n",
    "conn_info = dict()\n",
    "if os.path.isfile(config):\n",
    "    config = ConfigObj(config)\n",
    "    conn_info[\"sqluser\"] = config['username']\n",
    "    conn_info[\"sqlpass\"] = config['password']\n",
    "    conn_info[\"sqlhost\"] = config['host']\n",
    "    conn_info[\"sqlport\"] = config['port']\n",
    "    conn_info[\"dbname\"] = config['dbname']\n",
    "    conn_info[\"schema_name\"] = config['schema_name']\n",
    "else:\n",
    "    conn_info[\"sqluser\"] = 'postgres'\n",
    "    conn_info[\"sqlpass\"] = ''\n",
    "    conn_info[\"sqlhost\"] = 'localhost'\n",
    "    conn_info[\"sqlport\"] = 5432\n",
    "    conn_info[\"dbname\"] = 'eicu'\n",
    "    conn_info[\"schema_name\"] = 'public,eicu_crd'\n",
    "    \n",
    "# Connect to the eICU database\n",
    "print('Database: {}'.format(conn_info['dbname']))\n",
    "print('Username: {}'.format(conn_info[\"sqluser\"]))\n",
    "if conn_info[\"sqlpass\"] == '':\n",
    "    # try connecting without password, i.e. peer or OS authentication\n",
    "    try:\n",
    "        if (conn_info[\"sqlhost\"] == 'localhost') & (conn_info[\"sqlport\"]=='5432'):\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   user=conn_info[\"sqluser\"])            \n",
    "        else:\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   host=conn_info[\"sqlhost\"],\n",
    "                                   port=conn_info[\"sqlport\"],\n",
    "                                   user=conn_info[\"sqluser\"])\n",
    "    except:\n",
    "        conn_info[\"sqlpass\"] = getpass.getpass('Password: ')\n",
    "\n",
    "        con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                               host=conn_info[\"sqlhost\"],\n",
    "                               port=conn_info[\"sqlport\"],\n",
    "                               user=conn_info[\"sqluser\"],\n",
    "                               password=conn_info[\"sqlpass\"])\n",
    "query_schema = 'set search_path to ' + conn_info['schema_name'] + ';'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine a single patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "patientunitstayid = 141168"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>nursingchartid</th>\n",
       "      <th>nursingchartyear</th>\n",
       "      <th>nursingcharttime24</th>\n",
       "      <th>nursingcharttime</th>\n",
       "      <th>nursingchartoffset</th>\n",
       "      <th>nursingchartentryyear</th>\n",
       "      <th>nursingchartentrytime24</th>\n",
       "      <th>nursingchartentrytime</th>\n",
       "      <th>nursingchartentryoffset</th>\n",
       "      <th>nursingchartcelltypecat</th>\n",
       "      <th>nursingchartcelltypevallabel</th>\n",
       "      <th>nursingchartcelltypevalname</th>\n",
       "      <th>nursingchartvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141168</td>\n",
       "      <td>280649886</td>\n",
       "      <td>2015</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>midday</td>\n",
       "      <td>-594</td>\n",
       "      <td>2015</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>midday</td>\n",
       "      <td>-594</td>\n",
       "      <td>Other Vital Signs and Infusions</td>\n",
       "      <td>Abnormality Type-Skin Abnormality Arm Left Abr...</td>\n",
       "      <td>Value</td>\n",
       "      <td>Abrasion/Scratches</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141168</td>\n",
       "      <td>217584767</td>\n",
       "      <td>2015</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>6</td>\n",
       "      <td>Vital Signs</td>\n",
       "      <td>Non-Invasive BP</td>\n",
       "      <td>Non-Invasive BP Systolic</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141168</td>\n",
       "      <td>243815878</td>\n",
       "      <td>2015</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>6</td>\n",
       "      <td>Other Vital Signs and Infusions</td>\n",
       "      <td>Heart Rate Source</td>\n",
       "      <td>Value</td>\n",
       "      <td>Monitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141168</td>\n",
       "      <td>201844758</td>\n",
       "      <td>2015</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>6</td>\n",
       "      <td>Other Vital Signs and Infusions</td>\n",
       "      <td>Pulse Ox  Mode</td>\n",
       "      <td>Value</td>\n",
       "      <td>Continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141168</td>\n",
       "      <td>169700483</td>\n",
       "      <td>2015</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>6</td>\n",
       "      <td>Other Vital Signs and Infusions</td>\n",
       "      <td>MAP (mmHg)</td>\n",
       "      <td>Value</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patientunitstayid  nursingchartid  nursingchartyear nursingcharttime24  \\\n",
       "0             141168       280649886              2015           06:00:00   \n",
       "1             141168       217584767              2015           16:00:00   \n",
       "2             141168       243815878              2015           16:00:00   \n",
       "3             141168       201844758              2015           16:00:00   \n",
       "4             141168       169700483              2015           16:00:00   \n",
       "\n",
       "  nursingcharttime  nursingchartoffset  nursingchartentryyear  \\\n",
       "0           midday                -594                   2015   \n",
       "1          evening                   6                   2015   \n",
       "2          evening                   6                   2015   \n",
       "3          evening                   6                   2015   \n",
       "4          evening                   6                   2015   \n",
       "\n",
       "  nursingchartentrytime24 nursingchartentrytime  nursingchartentryoffset  \\\n",
       "0                06:00:00                midday                     -594   \n",
       "1                16:00:00               evening                        6   \n",
       "2                16:00:00               evening                        6   \n",
       "3                16:00:00               evening                        6   \n",
       "4                16:00:00               evening                        6   \n",
       "\n",
       "           nursingchartcelltypecat  \\\n",
       "0  Other Vital Signs and Infusions   \n",
       "1                      Vital Signs   \n",
       "2  Other Vital Signs and Infusions   \n",
       "3  Other Vital Signs and Infusions   \n",
       "4  Other Vital Signs and Infusions   \n",
       "\n",
       "                        nursingchartcelltypevallabel  \\\n",
       "0  Abnormality Type-Skin Abnormality Arm Left Abr...   \n",
       "1                                    Non-Invasive BP   \n",
       "2                                  Heart Rate Source   \n",
       "3                                     Pulse Ox  Mode   \n",
       "4                                         MAP (mmHg)   \n",
       "\n",
       "  nursingchartcelltypevalname   nursingchartvalue  \n",
       "0                       Value  Abrasion/Scratches  \n",
       "1    Non-Invasive BP Systolic                  82  \n",
       "2                       Value             Monitor  \n",
       "3                       Value          Continuous  \n",
       "4                       Value                  67  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select *\n",
    "from nursecharting\n",
    "where patientunitstayid = {}\n",
    "order by nursingchartoffset\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['patientunitstayid', 'nursingchartid', 'nursingchartyear',\n",
       "       'nursingcharttime24', 'nursingcharttime', 'nursingchartoffset',\n",
       "       'nursingchartentryyear', 'nursingchartentrytime24',\n",
       "       'nursingchartentrytime', 'nursingchartentryoffset',\n",
       "       'nursingchartcelltypecat', 'nursingchartcelltypevallabel',\n",
       "       'nursingchartcelltypevalname', 'nursingchartvalue'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nursingchartid</th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>nursingchartoffset</th>\n",
       "      <th>nursingchartentryoffset</th>\n",
       "      <th>nursingchartcelltypecat</th>\n",
       "      <th>nursingchartcelltypevallabel</th>\n",
       "      <th>nursingchartcelltypevalname</th>\n",
       "      <th>nursingchartvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>280649886</td>\n",
       "      <td>141168</td>\n",
       "      <td>-594</td>\n",
       "      <td>-594</td>\n",
       "      <td>Other Vital Signs and Infusions</td>\n",
       "      <td>Abnormality Type-Skin Abnormality Arm Left Abr...</td>\n",
       "      <td>Value</td>\n",
       "      <td>Abrasion/Scratches</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>217584767</td>\n",
       "      <td>141168</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Vital Signs</td>\n",
       "      <td>Non-Invasive BP</td>\n",
       "      <td>Non-Invasive BP Systolic</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>243815878</td>\n",
       "      <td>141168</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Other Vital Signs and Infusions</td>\n",
       "      <td>Heart Rate Source</td>\n",
       "      <td>Value</td>\n",
       "      <td>Monitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201844758</td>\n",
       "      <td>141168</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Other Vital Signs and Infusions</td>\n",
       "      <td>Pulse Ox  Mode</td>\n",
       "      <td>Value</td>\n",
       "      <td>Continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>169700483</td>\n",
       "      <td>141168</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Other Vital Signs and Infusions</td>\n",
       "      <td>MAP (mmHg)</td>\n",
       "      <td>Value</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nursingchartid  patientunitstayid  nursingchartoffset  \\\n",
       "0       280649886             141168                -594   \n",
       "1       217584767             141168                   6   \n",
       "2       243815878             141168                   6   \n",
       "3       201844758             141168                   6   \n",
       "4       169700483             141168                   6   \n",
       "\n",
       "   nursingchartentryoffset          nursingchartcelltypecat  \\\n",
       "0                     -594  Other Vital Signs and Infusions   \n",
       "1                        6                      Vital Signs   \n",
       "2                        6  Other Vital Signs and Infusions   \n",
       "3                        6  Other Vital Signs and Infusions   \n",
       "4                        6  Other Vital Signs and Infusions   \n",
       "\n",
       "                        nursingchartcelltypevallabel  \\\n",
       "0  Abnormality Type-Skin Abnormality Arm Left Abr...   \n",
       "1                                    Non-Invasive BP   \n",
       "2                                  Heart Rate Source   \n",
       "3                                     Pulse Ox  Mode   \n",
       "4                                         MAP (mmHg)   \n",
       "\n",
       "  nursingchartcelltypevalname   nursingchartvalue  \n",
       "0                       Value  Abrasion/Scratches  \n",
       "1    Non-Invasive BP Systolic                  82  \n",
       "2                       Value             Monitor  \n",
       "3                       Value          Continuous  \n",
       "4                       Value                  67  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at a subset of columns\n",
    "cols = ['nursingchartid','patientunitstayid',\n",
    "        'nursingchartoffset','nursingchartentryoffset',\n",
    "        'nursingchartcelltypecat', 'nursingchartcelltypevallabel',\n",
    "        'nursingchartcelltypevalname', 'nursingchartvalue']\n",
    "df[cols].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot patient vitals over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Heart Rate                                                                   49\n",
       "O2 Saturation                                                                33\n",
       "Temperature                                                                  32\n",
       "Heart Rate Source                                                            31\n",
       "Pulse Ox  Mode                                                               31\n",
       "Heart Rhythm                                                                 26\n",
       "Non-Invasive BP                                                              14\n",
       "Mental Status Assessment                                                     11\n",
       "Pain Evaluation at Rest                                                      10\n",
       "Invasive BP                                                                  10\n",
       "MAP (mmHg)                                                                   10\n",
       "Pain Score/Goal                                                              10\n",
       "Comforting Interventions                                                      9\n",
       "Edema Pre-tibial                                                              8\n",
       "Pulse Pedal                                                                   8\n",
       "BP Location                                                                   8\n",
       "Musculoskeletal Assessment                                                    8\n",
       "Integumentary Assessment Frequency                                            8\n",
       "Eye, Ear, Nose, Throat Assessment                                             8\n",
       "Respiratory Rate                                                              8\n",
       "Telemetry                                                                     8\n",
       "Edema Thigh                                                                   8\n",
       "Edema Pedal                                                                   8\n",
       "Mental Status Assessment Frequency                                            8\n",
       "Neurological Assessment Frequency                                             8\n",
       "Breath Sounds-RLL                                                             8\n",
       "Respiratory Assessment Frequency                                              8\n",
       "Respiratory Pattern-Objective                                                 8\n",
       "Integumentary Assessment                                                      8\n",
       "EENT Assessment Frequency                                                     8\n",
       "                                                                             ..\n",
       "Edema Arm                                                                     1\n",
       "Site Assessment-Non-Surgical Airway 01/23/15 Mask                             1\n",
       "Edema Hand                                                                    1\n",
       "Site Assessment-GI Tube 01/23/15 Nasogastric Right Nare                       1\n",
       "Support Systems                                                               1\n",
       "Dressing Assessment-Wound Leg Left Inner;Skin fold Tear                       1\n",
       "Dressing Assessment-Wound Abdomen Right Skin fold Excoriation                 1\n",
       "Speech                                                                        1\n",
       "Toileting Schedule                                                            1\n",
       "Participates in Fall Prevention Activities                                    1\n",
       "Site Care-Non-Surgical Airway 01/23/15 Mask                                   1\n",
       "Dressing Assessment-Wound Breast Right Skin fold Excoriation                  1\n",
       "O2 Admin Device                                                               1\n",
       "Site Assessment-Non-Surgical Airway 01/23/15 Oral ETT 7.5 mm Cuffed;Wired     1\n",
       "Site Assessment-Arterial Line Right Axilla                                    1\n",
       "Identified Barriers to Discharge/Transitions                                  1\n",
       "Site Care-Non-Surgical Airway 01/23/15 Oral ETT 7.5 mm Cuffed;Wired           1\n",
       "ADL Score                                                                     1\n",
       "Vision                                                                        1\n",
       "Abnormality Type-Skin Abnormality Arm Left Abrasion/Scratches                 1\n",
       "Dizziness                                                                     1\n",
       "Swallow                                                                       1\n",
       "Art Line Wave Form                                                            1\n",
       "Neck/Spine                                                                    1\n",
       "Type of Services Prior to Hospitalization                                     1\n",
       "O2 L/%                                                                        1\n",
       "Secured at (cm)-Non-Surgical Airway 01/23/15 Oral ETT 7.5 mm Cuffed;Wired     1\n",
       "Tube Status-GI Tube 01/23/15 Nasogastric Right Nare                           1\n",
       "Site Assessment-Central Venous Catheter 01/23/15 Right Jugular Triple         1\n",
       "Breath Sounds-Tracheal                                                        1\n",
       "Name: nursingchartcelltypevallabel, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vitals = df['nursingchartcelltypevallabel'].value_counts()\n",
    "vitals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iBFeuXMHIyIjPPvuMdu3albjvW7duceHCBXnXyzlz5jBjxgxatmwpj93IkSMB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\nzMzMrEH+P+6gyojOcaUeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cd1250198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list of lists\n",
    "# for each element, the list is:\n",
    "#   [nursingchartcelltypevallabel, nursingchartcelltypevalname]\n",
    "vitals = [['Heart Rate','Heart Rate'],\n",
    "          ['O2 Saturation','O2 Saturation'],\n",
    "          ['Temperature','Temperature (C)'],\n",
    "          ['Non-Invasive BP','Non-Invasive BP Systolic'],\n",
    "          ['Non-Invasive BP','Non-Invasive BP Diastolic'],\n",
    "          ['Invasive BP','Invasive BP Systolic'],\n",
    "          ['Invasive BP','Invasive BP Diastolic'],\n",
    "          ['MAP (mmHg)','Value']]\n",
    "\n",
    "plt.figure(figsize=[12,8])\n",
    "for v in vitals:\n",
    "    idx = (df['nursingchartcelltypevallabel'] == v[0]) & (df['nursingchartcelltypevalname'] == v[1])\n",
    "    df_plot = df.loc[idx, :]\n",
    "    \n",
    "    if 'Systolic' in v[1]:\n",
    "        marker = '^-'\n",
    "    elif 'Diastolic' in v[1]:\n",
    "        marker = 'v-'\n",
    "    else:\n",
    "        marker = 'o-'\n",
    "    plt.plot(df_plot['nursingchartoffset'],\n",
    "             pd.to_numeric(df_plot['nursingchartvalue'], errors='coerce'),\n",
    "            marker, markersize=8, lw=2, label=v)\n",
    "    \n",
    "plt.xlabel('Time since ICU admission (minutes)')\n",
    "plt.ylabel('Measurement value')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hospitals with data available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hospitalid</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_patients_with_tbl</th>\n",
       "      <th>data completion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>73</td>\n",
       "      <td>7059</td>\n",
       "      <td>7004</td>\n",
       "      <td>99.220853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>167</td>\n",
       "      <td>6092</td>\n",
       "      <td>5889</td>\n",
       "      <td>96.667761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>264</td>\n",
       "      <td>5237</td>\n",
       "      <td>5206</td>\n",
       "      <td>99.408058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>420</td>\n",
       "      <td>4679</td>\n",
       "      <td>4458</td>\n",
       "      <td>95.276769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>338</td>\n",
       "      <td>4277</td>\n",
       "      <td>4246</td>\n",
       "      <td>99.275193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>243</td>\n",
       "      <td>4243</td>\n",
       "      <td>4186</td>\n",
       "      <td>98.656611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>199</td>\n",
       "      <td>4240</td>\n",
       "      <td>4185</td>\n",
       "      <td>98.702830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>176</td>\n",
       "      <td>4328</td>\n",
       "      <td>4136</td>\n",
       "      <td>95.563771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>458</td>\n",
       "      <td>3701</td>\n",
       "      <td>3659</td>\n",
       "      <td>98.865172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>443</td>\n",
       "      <td>3656</td>\n",
       "      <td>3625</td>\n",
       "      <td>99.152079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     hospitalid  number_of_patients  number_of_patients_with_tbl  \\\n",
       "11           73                7059                         7004   \n",
       "54          167                6092                         5889   \n",
       "106         264                5237                         5206   \n",
       "184         420                4679                         4458   \n",
       "134         338                4277                         4246   \n",
       "90          243                4243                         4186   \n",
       "71          199                4240                         4185   \n",
       "58          176                4328                         4136   \n",
       "206         458                3701                         3659   \n",
       "200         443                3656                         3625   \n",
       "\n",
       "     data completion  \n",
       "11         99.220853  \n",
       "54         96.667761  \n",
       "106        99.408058  \n",
       "184        95.276769  \n",
       "134        99.275193  \n",
       "90         98.656611  \n",
       "71         98.702830  \n",
       "58         95.563771  \n",
       "206        98.865172  \n",
       "200        99.152079  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "with t as\n",
    "(\n",
    "select distinct patientunitstayid\n",
    "from nursecharting\n",
    ")\n",
    "select \n",
    "  pt.hospitalid\n",
    "  , count(distinct pt.patientunitstayid) as number_of_patients\n",
    "  , count(distinct t.patientunitstayid) as number_of_patients_with_tbl\n",
    "from patient pt\n",
    "left join t\n",
    "  on pt.patientunitstayid = t.patientunitstayid\n",
    "group by pt.hospitalid\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df['data completion'] = df['number_of_patients_with_tbl'] / df['number_of_patients'] * 100.0\n",
    "df.sort_values('number_of_patients_with_tbl', ascending=False, inplace=True)\n",
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tbs6AVlUvHWchkiRJ6gxzF+dOwB8DSweXr6p9RleWJEnS9BrmLs4vAJ+mGz3gxtGWI0mS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ccf132400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,6])\n",
    "plt.hist(df['data completion'], bins=np.linspace(0, 100, 10))\n",
    "plt.xlabel('Percent of patients with data in nursecharting')\n",
    "plt.ylabel('Number of hospitals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As is common in eICU-CRD, there are a subset of hospitals who routinely utilize this portion of the medical record (and thus have 90-100% data completion), while there are other hospitals who rarely use this interface and thus have poor data completion (0-10%)."
   ]
  }
 ],
 "metadata": {
  "front-matter": {
   "date": "2015-09-01",
   "linktitle": "admissiondrug",
   "title": "admissiondrug"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
